| Gradient descent algorithms for Bures-Wasserstein barycenters S Chewi, T Maunu, P Rigollet, AJ Stromme Conference on Learning Theory 2020 125, 1276-1304, 2020 | 59 | 2020 |
| Analysis of Langevin Monte Carlo from Poincaré to log-Sobolev S Chewi, MA Erdogdu, MB Li, R Shen, M Zhang Conference on Learning Theory, 1-2, 2022 | 40 | 2022 |
| SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence S Chewi, TL Gouic, C Lu, T Maunu, P Rigollet Advances in Neural Information Processing Systems 33, 2098-2109, 2020 | 37 | 2020 |
| Optimal dimension dependence of the Metropolis-adjusted Langevin algorithm S Chewi, C Lu, K Ahn, X Cheng, T Le Gouic, P Rigollet Conference on Learning Theory, 1260-1300, 2021 | 35 | 2021 |
| Efficient constrained sampling via the mirror-Langevin algorithm K Ahn, S Chewi Advances in Neural Information Processing Systems 34, 28405-28418, 2021 | 31 | 2021 |
| Exponential ergodicity of mirror-Langevin diffusions S Chewi, TL Gouic, C Lu, T Maunu, P Rigollet, A Stromme Advances in Neural Information Processing Systems 33, 19573-19585, 2020 | 30 | 2020 |
| Towards a theory of non-log-concave sampling: first-order stationarity guarantees for Langevin Monte Carlo K Balasubramanian, S Chewi, MA Erdogdu, A Salim, M Zhang Conference on Learning Theory, 2896-2923, 2022 | 24 | 2022 |
| Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent J Altschuler, S Chewi, PR Gerber, A Stromme Advances in Neural Information Processing Systems 34, 22132-22145, 2021 | 24 | 2021 |
| Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions S Chen, S Chewi, J Li, Y Li, A Salim, AR Zhang arXiv preprint arXiv:2209.11215, 2022 | 23 | 2022 |
| Dimension-free log-Sobolev inequalities for mixture distributions HB Chen, S Chewi, J Niles-Weed Journal of Functional Analysis 281 (11), 109236, 2021 | 17 | 2021 |
| Improved analysis for a proximal algorithm for sampling Y Chen, S Chewi, A Salim, A Wibisono Conference on Learning Theory, 2984-3014, 2022 | 16 | 2022 |
| Variational inference via Wasserstein gradient flows M Lambert, S Chewi, F Bach, S Bonnabel, P Rigollet arXiv preprint arXiv:2205.15902, 2022 | 15 | 2022 |
| Fast and smooth interpolation on Wasserstein space S Chewi, J Clancy, T Le Gouic, P Rigollet, G Stepaniants, A Stromme International Conference on Artificial Intelligence and Statistics, 3061-3069, 2021 | 12 | 2021 |
| The query complexity of sampling from strongly log-concave distributions in one dimension S Chewi, PR Gerber, C Lu, T Le Gouic, P Rigollet Conference on Learning Theory, 2041-2059, 2022 | 8 | 2022 |
| The entropic barrier is -self-concordant S Chewi arXiv preprint arXiv:2112.10947, 2021 | 8 | 2021 |
| Log-concave sampling S Chewi Book draft available at https://chewisinho.github.io, 2023 | 7 | 2023 |
| Learning threshold neurons via the "edge of stability" K Ahn, S Bubeck, S Chewi, YT Lee, F Suarez, Y Zhang arXiv preprint arXiv:2212.07469, 2022 | 7 | 2022 |
| An entropic generalization of Caffarelli's contraction theorem via covariance inequalities S Chewi, AA Pooladian arXiv preprint arXiv:2203.04954, 2022 | 5 | 2022 |
| Faster high-accuracy log-concave sampling via algorithmic warm starts JM Altschuler, S Chewi arXiv preprint arXiv:2302.10249, 2023 | 4 | 2023 |
| Improved discretization analysis for underdamped Langevin Monte Carlo M Zhang, S Chewi, MB Li, K Balasubramanian, MA Erdogdu arXiv preprint arXiv:2302.08049, 2023 | 3 | 2023 |
Philippe RigolletMassachusetts Institute of TechnologyVerified email at math.mit.edu
